ClearTK 2.0: Design Patterns for Machine Learning in UIMA.

Steven Bethard, Philip Ogren, Lee Becker
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Abstract

ClearTK adds machine learning functionality to the UIMA framework, providing wrappers to popular machine learning libraries, a rich feature extraction library that works across different classifiers, and utilities for applying and evaluating machine learning models. Since its inception in 2008, ClearTK has evolved in response to feedback from developers and the community. This evolution has followed a number of important design principles including: conceptually simple annotator interfaces, readable pipeline descriptions, minimal collection readers, type system agnostic code, modules organized for ease of import, and assisting user comprehension of the complex UIMA framework.

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ClearTK 2.0: UIMA中机器学习的设计模式
ClearTK将机器学习功能添加到UIMA框架中,为流行的机器学习库提供包装器,为不同分类器提供丰富的特征提取库,以及用于应用和评估机器学习模型的实用程序。自2008年成立以来,ClearTK根据开发人员和社区的反馈不断发展。这种演变遵循了许多重要的设计原则,包括:概念上简单的注释器接口、可读的管道描述、最小的集合读取器、与类型系统无关的代码、为便于导入而组织的模块,以及帮助用户理解复杂的UIMA框架。
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